Close
Metadata

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3RMKN22
Repositorysid.inpe.br/sibgrapi/2018/08.24.16.12
Last Update2018:08.24.16.12.54 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2018/08.24.16.12.54
Metadata Last Update2022:06.14.00.09.06 (UTC) administrator
DOI10.1109/SIBGRAPI.2018.00030
Citation KeyBentoSouzFray:2018:AuApEv
TitleMulticenter Imaging Studies: Automated Approach to Evaluating Data Variability and the Role of Outliers
FormatOn-line
Year2018
Access Date2024, Apr. 28
Number of Files1
Size1017 KiB
2. Context
Author1 Bento, Mariana
2 Souza, Roberto
3 Frayne, Richard
Affiliation1 University of Calgary
2 University of Calgary
3 University of Calgary
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addressmarianapbento@gmail.com
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2018-08-31 15:33:34 :: marianapbento@gmail.com -> administrator :: 2018
2022-06-14 00:09:06 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsmulticenter MR data
outlier detection
data variability
AbstractMagnetic resonance (MR) as well as other imaging modalities have been used in a large number of clinical and research studies for the analysis and quantification of important structures and the detection of abnormalities. In this context, machine learning is playing an increasingly important role in the development of automated tools for aiding in image quantification, patient diagnosis and follow-up. Normally, these techniques require large, heterogeneous datasets to provide accurate and generalizable results. Large, multi-center studies, for example, can provide such data. Images acquired at different centers, however, can present varying characteristics due to differences in acquisition parameters, site procedures and scanners configuration. While variability in the dataset is required to develop robust, generalizable studies (i.e., independent of the acquisition parameters or center), like all studies there is also a need to ensure overall data quality by prospectively identifying and removing poor-quality data samples that should not be included, e.g., outliers. We wish to keep image samples that are representative of the underlying population (so called inliers), yet removing those samples that are not. We propose a framework to analyze data variability and identify samples that should be removed in order to have more representative, reliable and robust datasets. Our example case study is based on a public dataset containing T1-weighted volumetric head images data acquired at six different centers, using three different scanner vendors and at two commonly used magnetic fields strengths. We propose an algorithm for assessing data robustness and finding the optimal data for study occlusion (i.e., the data size that presents with lowest variability while maintaining generalizability (i.e., using samples from all sites)).
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2018 > Multicenter Imaging Studies:...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Multicenter Imaging Studies:...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 24/08/2018 13:12 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3RMKN22
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RMKN22
Languageen
Target File57_manuscript.pdf
User Groupmarianapbento@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 14
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


Close